Honeywell International Inc.

Honeywell G.m.b.h.

Michael Ray Elgersma

Kevin Raymond Driscoll

Yunqian Ma

Cleopatra Cabuz

Brendan Hall

Vibhor L Bageshwar

Blaise Grayson Morton

Eric A Euteneuer

Benjamin Mohr

Daniel P Johnson

Ryan Douglas Ingvalson

Kevin John Stalsberg

Thomas R Ohnstein

Steven H Thomas

Randolph G Hartman

Mason A Peck

Ruth Dagmar Kreichauf

Subhabrata Ganguli

Gunter Stein

Dennice F Gayme

Brian E Fly

Theresa C Jenne

Steven P Cienciwa

Saad J Bedros

Brian H Hamilton

Wesley J Hawkinson

Kartik B Ariyur

Wayne A Soehren

Ben Miller

John B McKitterick

Nick Anthony Demma

Gordon F Rouse

John Burt McKitterick

LeRoy Ernest Vetsch

Brian K Underhill

Ross Merritt

Christopher Jay Matthews

Richard S Russell

Kingsley O C Fregene

Robert B Smith

Paul Samanant

Michael William Greenwood

Tom Jakel

Xiaohong Linda Li

Stephen G Pratt

Balaji Mahadev

Brian J Hamilton

Michael Janssen

Stephen Artie Rooks

Eric Lautenschlager

Samar Dajani-Brown

A method for planning a vehicle trajectory is provided. The method comprises obtaining an edge map representation corresponding to one or more terrain images of a given area, and identifying a total number of edge pixels in each of a plurality of sub-regions of the edge map representation. The method further comprises determining a measurement probability density function (PDF) for each of the sub-regions based on the number of edge pixels with information content in each sub-region. The method then computes a trajectory cost for each of the sub-regions by dividing a user-selected scalar by a sum of: a user-selected value and the number of edge pixels with information content in each sub-region. Thereafter, the method selects a trajectory for navigation of a vehicle over the given area based on the trajectory cost for each of the sub-regions.

Method and system for planning vehicle trajectories by enhancing en route navigation performance

Systems and methods for operating a navigation system and detecting GNSS spoofing using a chip-scale atomic clock are provided herein.

Systems and methods using chip-scale atomic clock to detect spoofed GNSS

A system for integrity monitoring comprises a processor onboard a vehicle, a terrain map database, vision sensors, and inertial sensors. Each vision sensor has an overlapping FOV with at least one other vision sensor. The processor performs integrity monitoring of kinematic states of the vehicle, and comprises a plurality of first level filter sets, each including multiple position PDF filters. A consistency monitor is coupled to each first level filter set. Second level filter sets, each including multiple position PDF filters and a navigation filter, are coupled to the consistency monitor, the map database, the vision sensors, and the inertial sensors. Consistency check modules are each coupled to a respective second level filter set. A third level fusion filter is coupled to each of the consistency check modules. An integrity monitor is coupled to the fusion filter and assures integrity of a final estimate of the vehicle kinematic state statistics.

Integrity monitoring of vehicle kinematic states using map-based, vision navigation systems

A navigation system for a vehicle comprises onboard sensors including a vision sensor, and an onboard map database of terrain maps. An onboard processer, coupled to the sensors and map database, includes a position PDF filter, which performs a method comprising: receiving image data from the vision sensor corresponding to terrain images captured by the vision sensor of a given area; receiving map data from the map database corresponding to a terrain map of the area; generating a first PDF of image features in the image data; generating a second PDF of map features in the map data; generating a measurement vector PDF by a convolution of the first PDF and second PDF; estimating a position vector PDF using a non-linear filter that receives the measurement vector PDF; and generating statistics from the estimated position vector PDF that include real-time measurement errors of position and angular orientation of the vehicle.

Position probability density function filter to determine real-time measurement errors for map based, vision navigation systems

A method of assuring integrity of range measurements and position solutions comprises obtaining range measurement statistics between network nodes, performing a snapshot integrity test for the range measurement statistics, and performing a sequential integrity test using the range measurement statistics. The snapshot integrity test comprises using Gram matrices with a current configuration of the nodes; performing a singular value consistency check using the Gram matrices against a user selected threshold; and detecting and excluding range measurement statistics with instantaneous errors that cause the singular value to exceed the threshold. The sequential integrity test comprises formulating main node and sub-node sets using solution separation; implementing filters for the main node and sub-node sets; performing a consistency check using discriminators and decision thresholds; detecting and excluding range measurement statistics with both instantaneous and time-correlated errors; and computing a protection level for relative positions computed from the main node and sub-node sets.

Integrity of range measurements

A multiple faulty global navigation satellite signal detecting system is provided. The system includes at least one pair of spaced antennas, at least one aiding source and processor. The at least one pair of spaced antennas are configured to receive satellite signals from a plurality of satellites. The at least one aiding source is used to generate aiding source position estimate signals. The processor is in communication with each antenna and the at least one aiding source. The processor is configured to determine signals blocks. The signal blocks being a collection of subsets of the determined difference signals and a covariance matrix for the difference signals. The processor further configured to generate a union of good signals from all the good blocks and a complementary set of bad signals.

Multiple faulty global navigation satellite system signal detecting system

In one embodiment, a method of tracking multiple objects with a probabilistic hypothesis density filter is provided. The method includes obtaining measurements corresponding to a first object with at least one sensor, the at least one sensor providing one or more first track IDs for the measurements. A Tfirst predicted intensity is generated for the first object based on a Tfirst track intensity. A Tmeasurement from a first sensor of the at least one sensors is obtained, the first sensor providing a second track ID for the Tmeasurement. The second track ID is compared to the one or more first track IDs, and the Tfirst predicted intensity is selectively updated with the Tmeasurement based on whether the second track ID matches any of the one or more first track IDs to generate a Tfirst measurement-to-track intensity for the first object.

Updating intensities in a PHD filter based on a sensor track ID

A method of determining at least one of position and attitude in relation to an object is provided. The method includes capturing at least two images of the object with at least one camera. Each image is captured at a different position in relation to the object. The images are converted to edge images. The edge images of the object are converted into three-dimensional edge images of the object using positions of where the at least two images were captured. Overlap edge pixels in the at least two three-dimensional edge images are located to identify overlap points. A three dimensional edge candidate point image of the identified overlapped points in an evidence grid is built. The three dimensional candidate edge image in the evidence grid is compared with a model of the object to determine at least one of a then current position and attitude in relation to the object.

Position and attitude determination method and system using edge images

In one embodiment, a method of tracking multiple objects with a probabilistic hypothesis density filter is provided. The method includes providing a plurality of intensities. The plurality of intensities are pruned based on their respective weight to remove lower weighted intensities and produce a first set of intensities. Intensities in the first set of intensities that are identified as corresponding to the same object are then merged to produce a second set of intensities. The second set of intensities is then pruned again to produce a final set of intensities, wherein pruning the second set of intensities includes in the final set of intensities, up to a threshold number of intensities having the largest weights in the second set of intensities and excludes from the final set of intensities any remaining intensities in the second set of intensities.

Two step pruning in a PHD filter

A method for compressing three-dimensional data points is disclosed. The method includes measuring a plurality of three-dimensional data points using one or more sensors communicatively coupled to the computing device, where each three-dimensional data point represents a point in a three-dimensional environment. The method also includes dividing the three-dimensional environment into a plurality of spatial cubes, wherein each three-dimensional data point is mapped to one spatial cube. The method also includes, for each spatial cube, converting the three-dimensional data points in the respective spatial cube to a two-dimensional plane based on a number of three-dimensional data points in the respective spatial cube. The method also includes, for each two-dimensional plane, determining polygon vertices of a planar polygon at points where an edge of an associated spatial cube intersects the respective two-dimensional plane. The method also includes sending the polygon vertices of each planar polygon to a ground station.

Compressing data points into polygons

A method of mitigating filter inconsistency of a heading estimate in an inertial navigation system is provided. The method includes inputting inertial measurements from at least one low-performance inertial measurement unit (IMU) in the inertial navigation system; inputting an initial-heading from at least one heading-information source; inputting an initial-heading uncertainty level associated with an error in the inputted initial-heading; initializing an initial-heading estimate with the inputted initial-heading; initializing an initial-heading uncertainty estimate with the inputted initial-heading uncertainty level; initializing an accumulated heading-change estimate to zero at a startup of the at least one low-performance IMU; and initializing an accumulated heading-change uncertainty estimate with an initial accumulated heading-change uncertainty level that is a value less than the inputted initial-heading uncertainty level; and periodically updating the accumulated heading-change estimate with the inertial measurements input from the at least one low-performance IMU.

Heading inconsistency mitigation for inertial navigation using low-performance inertial measurement units with relative aiding

A method of generating a map for image aided navigation comprises detecting one or more edges in one or more terrain images, and obtaining a compressed edge representation of the one or more edges. The compressed edge representation is obtained by a method comprising fitting a geometric form or other mathematical model to the one or more edges, calculating one or more coefficients based on the fitted geometric form or other mathematical model, and storing the one or more coefficients in a database for use in generating the map for image aided navigation.

Compressed edge map representation for image aided navigation

In one embodiment, a method of tracking multiple objects with a probabilistic hypothesis density filter is provided. The method includes generating a first intensity by combining a first one or more measurements, wherein a first set of track IDs associated with the first intensity includes track IDs corresponding to respective measurements in the first one or more measurements. A second intensity is generated by combining a second one or more measurements, wherein a second set of track IDs associated with the second intensity includes track IDs corresponding to respective measurements in the second one or more measurements. The first set of track IDs is compared to the second set of track IDs, and the first intensity is selectively merged with the second intensity based on whether any track IDs in the first set of track IDs match any track IDs in the second set of track IDs.

Merging intensities in a PHD filter based on a sensor track ID

Growing community of inventors

Plymouth, MN, United States of America

Michael Ray Elgersma